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National identity predicts public health support during a global pandemic (vol 13, 517, 2022)

NATURE COMMUNICATIONS 2022年第1期13卷 517-517页

作者： Van Bavel, Jay J. Cichocka, Aleksandra Capraro, Valerio Sjastad, Hallgeir Nezlek, John B. Pavlovic, Tomislav Alfano, Mark Gelfand, Michele J. Azevedo, Flavio Birtel, Michele D. Cislak, Aleksandra Lockwood, Patricia L. Ross, Robert Malcolm Abts, Koen Agadullina, Elena Aruta, John Jamir Benzon Besharati, Sahba Nomvula Bor, Alexander Choma, Becky L. Crabtree, Charles David Cunningham, William A. De, Koustav Ejaz, Waqas Elbaek, Christian T. Findor, Andrej Flichtentrei, Daniel Franc, Renata Gjoneska, Biljana Gruber, June Gualda, Estrella Horiuchi, Yusaku Huynh, Toan Luu Duc Ibanez, Agustin Imran, Mostak Ahamed Israelashvili, Jacob Jasko, Katarzyna Kantorowicz, Jaroslaw Kantorowicz-Reznichenko, Elena Krouwel, Andre Laakasuo, Michael Lamm, Claus Leygue, Caroline Lin, Ming-Jen Mansoor, Mohammad Sabbir Marie, Antoine Mayiwar, Lewend Mazepus, Honorata McHugh, Cillian Minda, John Paul Mitkidis, Panagiotis Olsson, Andreas Otterbring, Tobias Packer, Dominic J. Perry, Anat Petersen, Michael Bang Puthillam, Arathy Riano-Moreno, Julian C. Rothmund, Tobias Santamaria-Garcia, Hernando Schmid, Petra C. Stoyanov, Drozdstoy Tewari, Shruti Todosijevic, Bojan Tsakiris, Manos Tung, Hans H. Umbres, Radu G. Vanags, Edmunds Vlasceanu, Madalina Vonasch, Andrew Yucel, Meltem Zhang, Yucheng Abad, Mohcine Adler, Eli Akrawi, Narin Mdarhri, Hamza Alaoui Amara, Hanane Amodio, David M. Antazo, Benedict G. Apps, Matthew Ay, F. Ceren Ba, Mouhamadou Hady Barbosa, Sergio Bastian, Brock Berg, Anton Bernal-Zarate, Maria P. Bernstein, Michael Bialek, Michal Bilancini, Ennio Bogatyreva, Natalia Boncinelli, Leonardo Booth, Jonathan E. Borau, Sylvie Buchel, Ondrej Cameron, C. Daryl Carvalho, Chrissie F. Celadin, Tatiana Cerami, Chiara Chalise, Hom Nath Cheng, Xiaojun Cian, Luca Cockcroft, Kate Conway, Jane Cordoba-Delgado, Mateo Andres Crespi, Chiara Crouzevialle, Marie Cutler, Jo Cypryanska, Marzena Dabrowska, Justyna Daniels, Michael A. Davis, Victoria H. Dayley, Pamala N. Delouvee, Sylvain Denkovski, Ognjan Dezecache, Guillaume Dhaliwal, Nathan A. Diato, Department of Psychology and Neural Science New York University New York NY USA School of Psychology University of Kent Canterbury England Department of Economics Middlesex University London London England Department of Strategy and Management Norwegian School of Economics Bergen Norway SWPS University of Social Sciences and Humanities Poznań Poland Department of Psychological Sciences College of William and Mary Williamsburg VA USA Institute of Social Sciences Ivo Pilar Zagreb Croatia Department of Philosophy Macquarie University Sydney NSW Australia Stanford Graduate School of Business Stanford University Stanford CA USA Institute of Communication Science Friedrich-Schiller University Jena Jena Germany School of Human Sciences Institute for Lifecourse Development University of Greenwich London England Department of Experimental Psychology University of Oxford Oxford England Center for Human Brain Health School of Psychology University of Birmingham Birmingham England Department of Psychology Macquarie University Sydney NSW Australia KU Leuven Leuven Belgium National Research University Higher School of Economics (HSE) Moscow Russia De La Salle University Manila Philippines Department of Psychology University of the Witwatersrand Johannesburg South Africa Department of Political Science Aarhus University Aarhus Denmark X University Toronto Canada Department of Government Dartmouth College Hanover NH USA Department of Psychology University of Toronto Toronto ON Canada Gatton College of Business and Economics University of Kentucky Lexington KY USA Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan Department of Management Aarhus University Aarhus Denmark Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia IntraMed Buenos Aires Argentina Macedonian Academy of Sciences and Arts North Macedonia Republic of North Macedonia University of Colorado Boulder Bould

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics. Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.

关键词： Viral infection human behaviour

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White Matter Deficits Underlying the Impaired Consciousness Level in Patients with Disorders of Consciousness

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Neuroscience Bulletin 2018年第4期34卷 668-678页

作者： Xuehai Wu Jiaying Zhang Zaixu Cui Weijun Tang Chunhong Shao Jin Hu Jianhong Zhu Yao Zhao Lu Lu Gang Chen Georg Northoff Gaolang Gong Ying Mao Yong He Neurosurgical Department Shanghai Huashan HospitalFudan University State Key Laboratory of Cognitive Neuroscience and Learning IDG/Mc Govern Institute for Brain ResearchBeijing Normal University Centre for Medical Image Computing Department of Computer Science University College London Radiological Department Shanghai Huashan Hospital Fudan University Psychiatry Department Shanghai Huashan Hospital Fudan University Huajia Hospital Shanghai Scientific and Statistical Computing Core National Institute of Mental Health National Institutes of Health Department of Health and Human Services Institute of Mental Health Research University of Ottawa

In this study, we aimed to （1） identify white matter （WM） deficits underlying the consciousness level in patients with disorders of consciousness （DOCs） using diffusion tensor imaging （DTI）, and （2） evaluate the relationship between DTI metrics and clinical measures of the consciousness level in DOC patients. With a cohort of 8 comatose, 8 unresponsive wakefulness syndrome/ vegetative state, and 14 minimally conscious state patients and 25 patient controls, we performed group comparisons of the DTI metrics in 48 core WM regions of interest （ROIs）, and examined the clinical relevance using correlation analysis. We identified multiple abnormal WM ROIs in DOC patients compared with normal controls, and the DTI metrics in these ROIs were significantly correlated with clinical measures of the consciousness level. Therefore, our findings suggested that multiple WM tracts are involved in the impaired consciousness levels in DOC patients and demonstrated the clinical relevance of DTI for DOC patients.

关键词： Disorder of consciousness White matter Diffusion tensor imaging Brain injury

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Joint I-Vector with End-to-End System for Short Duration Text-Independent Speaker Verification

Joint I-Vector with End-to-End System for Short Duration Tex...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zili Huang Shuai Wang Yanmin Qian Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538646595

Factor analysis based i-vector has been the state-of-the-art method for speaker verification. Recently, researchers propose to build DNN based end-to-end speaker verification systems and achieve comparable performance with i -vector. Since these two methods possess their own property and differ from each other significantly, we explore a framework to integrate these two paradigms together to utilize their complementarity. More specifically, in this paper we develop and compare four methodologies to integrate traditional i -vector into end-to-end systems, including score fusion, embeddings concatenation, transformed concatenation and joint learning. All these approaches achieve significant gains. Moreover, the hard trial selection is performed on the end-to-end architecture which further improves the performance. Experimental results on a text-independent short-duration dataset generated from SRE 2010 reveal that the newly proposed method reduces the EER by relative 31.0% and 28.2% compared to the i-vector and end-to-end baselines respectively.

关键词： speaker verification end-to-end i-vector triplet loss hard trial selection speaker recognition end-to-end end system Loudspeakers Joints energy efficiency ratio Short circuits

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FOCAL KL-DIVERGENCE BASED DILATED CONVOLUTIONAL NEURAL NETWORKS FOR CO-CHANNEL SPEAKER IDENTIFICATION

FOCAL KL-DIVERGENCE BASED DILATED CONVOLUTIONAL NEURAL NETWO...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shuai Wang Yanmin Qian Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538646595

Recognizing the identities of multiple talkers via their overlapped speech is a challenging task, it is also one main difficulty for the "cocktail party problem". In this paper, a novel dilated convolutional neural network with a focal KL-divergence loss function is proposed to tackle this problem. During training, relative loss for the well-classified samples is automatically reduced and consequently more attention is paid to the hard samples. The use of the focal KL-divergence loss function leads to more stable training and improved testing performance. Furthermore, a post processing of assigning different frames with different weights is also adopted and leads to further improvement. The proposed framework can be easily extended from 2-talker to 3-talker speaker identification scenario. Experiments on the artificially generated RSR2015 multi-talker mixed corpus show that the proposed approach can improve multi-talker speaker identification significantly.

关键词： co-channel speaker identification convolutional neural networks focal loss dilated convolution Loudspeakers Neural network speaker recognition personal identity

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Deep discriminant analysis for I-vector based robust speaker recognition

arXiv

引用

arXiv 2018年

作者： Wang, Shuai Huang, Zili Qian, Yanmin Yu, Kai Key Laboratory of Shanghai Education Commission for Intelligent Interaction Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Linear Discriminant Analysis (LDA) has been used as a standard post-processing procedure in many state-of-the-art speaker recognition tasks. Through maximizing the inter-speaker difference and minimizing the intra-speaker variation, LDA projects i-vectors to a lower-dimensional and more discriminative subspace. In this paper, we propose a neural network based compensation scheme(termed as deep discriminant analysis, DDA) for i-vector based speaker recognition, which shares the spirit with LDA. Optimized against softmax loss and center loss at the same time, the proposed method learns a more compact and discriminative embedding space. Compared with the Gaussian distribution assumption of data and the learnt linear projection in LDA, the proposed method doesn't pose any assumptions on data and can learn a non-linear projection function. Experiments are carried out on a short-duration text-independent dataset based on the SRE Corpus, noticeable performance improvement can be observed against the normal LDA or PLDA methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Discriminant analysis

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SEMI-SUPERVISED TRAINING USING ADVERSARIAL MULTI-TASK LEARNING FOR SPOKEN LANGUAGE UNDERSTANDING

SEMI-SUPERVISED TRAINING USING ADVERSARIAL MULTI-TASK LEARNI...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ouyu Lan Su Zhu Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538646595

Spoken language understanding (SLU) usually requires human semantic annotation on collected data, but the process is expensive. In order to make better use of unlabeled data for robust SLU, we propose an adversarial multi-task learning method by merging a bidirectional language model (BLM) and a slot tagging model (STM). As a secondary objective, the BLM is used to learn generalized and unsupervised knowledge with abundant unlabeled data and improve the performance of STM on unseen data samples. We construct a shared space for both tasks and independent private spaces for each task respectively. Additional adversarial task discriminator is also used to obtain more task-independent sharing information. Experiments show that the proposed approaches achieve the state-of-the-art performance on the small scale ATIS benchmark and significantly improve the semi-supervised performance on a large-scale dataset.

关键词： Spoken language understanding Multitask learning Semi-supervised learning Adversarial task discriminator spoken language understanding Semi-supervised Learning STACK TRITIUM MONITOR Scanning Tunneling Microscope Data sampling BLM gene Data Collection Methods

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Robust Spoken Language Understanding with Unsupervised ASR-Error Adaptation

Robust Spoken Language Understanding with Unsupervised ASR-E...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Su Zhu Ouyu Lan Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538646595

Robustness to errors produced by automatic speech recognition (ASR) is essential for Spoken Language Understanding (SLU). Traditional robust SLU typically needs ASR hypotheses with semantic annotations for training. However, semantic annotation is very expensive, and the corresponding ASR system may change frequently. Here, we propose a novel unsupervised ASR-error adaptation method, obviating the need of annotated ASR hypotheses. It only requires semantically annotated transcripts for the slot-tagging task and the transcripts paired with hypotheses for an input sentence reconstruction task. In this method, feature encoders which share part of the parameters are exploited to enforce the tasks in a similar feature space. Therefore, the transcript side slot-tagging model can be transferred to ASR hypotheses side easily. Experiments show that the proposed approach can yield significant improvement over strong baselines, and achieve performance very close to the oracle system.

关键词： Spoken Language understanding ASR-error robustness adversarial adaptation spoken language understanding Speech recognition aquifer storage and recovery spoken language Eigenvector transcript Religious Missions ANNOTATIONS

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Author Correction: New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

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Nature genetics 2024年第5期56卷 1032-1033页

作者： Nick Shrine Anna L Guyatt A Mesut Erzurumluoglu Victoria E Jackson Brian D Hobbs Carl A Melbourne Chiara Batini Katherine A Fawcett Kijoung Song Phuwanat Sakornsakolpat Xingnan Li Ruth Boxall Nicola F Reeve Ma'en Obeidat Jing Hua Zhao Matthias Wielscher Stefan Weiss Katherine A Kentistou James P Cook Benjamin B Sun Jian Zhou Jennie Hui Stefan Karrasch Medea Imboden Sarah E Harris Jonathan Marten Stefan Enroth Shona M Kerr Ida Surakka Veronique Vitart Terho Lehtimäki Richard J Allen Per S Bakke Terri H Beaty Eugene R Bleecker Yohan Bossé Corry-Anke Brandsma Zhengming Chen James D Crapo John Danesh Dawn L DeMeo Frank Dudbridge Ralf Ewert Christian Gieger Amund Gulsvik Anna L Hansell Ke Hao Joshua D Hoffman John E Hokanson Georg Homuth Peter K Joshi Philippe Joubert Claudia Langenberg Xuan Li Liming Li Kuang Lin Lars Lind Nicholas Locantore Jian'an Luan Anubha Mahajan Joseph C Maranville Alison Murray David C Nickle Richard Packer Margaret M Parker Megan L Paynton David J Porteous Dmitry Prokopenko Dandi Qiao Rajesh Rawal Heiko Runz Ian Sayers Don D Sin Blair H Smith María Soler Artigas David Sparrow Ruth Tal-Singer Paul R H J Timmers Maarten Van den Berge John C Whittaker Prescott G Woodruff Laura M Yerges-Armstrong Olga G Troyanskaya Olli T Raitakari Mika Kähönen Ozren Polašek Ulf Gyllensten Igor Rudan Ian J Deary Nicole M Probst-Hensch Holger Schulz Alan L James James F Wilson Beate Stubbe Eleftheria Zeggini Marjo-Riitta Jarvelin Nick Wareham Edwin K Silverman Caroline Hayward Andrew P Morris Adam S Butterworth Robert A Scott Robin G Walters Deborah A Meyers Michael H Cho David P Strachan Ian P Hall Martin D Tobin Louise V Wain Department of Health Sciences University of Leicester Leicester UK. Population Health and Immunity Division The Walter and Eliza Hall Institute of Medical Research Parkville Victoria Australia. Department of Medical Biology University of Melbourne Parkville Victoria Australia. Channing Division of Network Medicine Brigham and Women's Hospital Boston MA USA. Division of Pulmonary and Critical Care Medicine Brigham and Women's Hospital Boston MA USA. Target Sciences GlaxoSmithKline Collegeville PA USA. Department of Medicine Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand. Division of Genetics Genomics and Precision Medicine Department of Medicine University of Arizona Tucson AZ USA. Nuffield Department of Population Health University of Oxford Oxford UK. Medical Research Council Population Health Research Unit University of Oxford Oxford UK. The University of British Columbia Centre for Heart Lung Innovation St Paul's Hospital Vancouver British Columbia Canada. Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Department of Epidemiology and Biostatistics MRC-PHE Centre for Environment & Health School of Public Health Imperial College London London UK. Interfaculty Institute for Genetics and Functional Genomics Department of Functional Genomics University Medicine Greifswald Greifswald Germany. Centre for Global Health Research Usher Institute for Population Health Sciences and Informatics University of Edinburgh Edinburgh UK. Centre for Cardiovascular Sciences Queen's Medical Research Institute University of Edinburgh Edinburgh UK. Department of Biostatistics University of Liverpool Liverpool UK. MRC/BHF Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Flatiron Institute Simons Foundation New York NY USA. Busselton Population Medical Research Institute Sir Charles Gairdner Hospital N

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Particle swarm and population structure 18

Particle swarm and population structure

引用

2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Fernandes, Carlos M. Rosa, Agostinho C. Fachada, Nuno Laredo, J.L.J. Merelo, J.J. LARSyS: Laboratory for Robotics and Systems in Engineering and Science University of Lisbon Portugal HEI-LAB - Digital Human-Environment and Interactions Labs Universidade Lusófona Lisbon Portugal LITIS University of Le Havre Le Havre France Department of Computer Architecture University of Granada Granada Spain

ISBN: (纸本)9781450357647

We investigate the convergence speed, accuracy, robustness and scalability of PSOs structured by regular and random graphs with 3 ≤ k ≤ n. The main conclusion is that regular and random graphs with the same averaged connectivity may result in significantly different performance, namely when is low. © 2018 Copyright is held by the owner/author(s).

关键词： Particle swarm optimization (PSO)

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